Practice with tidy data
Ex. 1 Religion
pew %>% gather(2:7, key = "income_bracket", value = "count")
Ex. 2 Billboard
billboard %>% gather(6:81, key = "week", value = "rank")
Ex. 3 Weather
weather %>% gather(d1:d8, key = "day", value = "temp") %>% spread(key = "element", value = "temp")
Extending tidy data
Extending tidy data
- If you have one column that contains two variables, they need to be
separate()ed. - If you have multiple columns that contain one variable, they need to be
unite()ed.
Separate()
A
## # A tibble: 6 x 3 ## country year rate ## * <chr> <int> <chr> ## 1 Afghanistan 1999 745/19987071 ## 2 Afghanistan 2000 2666/20595360 ## 3 Brazil 1999 37737/172006362 ## 4 Brazil 2000 80488/174504898 ## 5 China 1999 212258/1272915272 ## 6 China 2000 213766/1280428583
A %>%
separate(rate, into = c("cases", "population"))
## # A tibble: 6 x 4 ## country year cases population ## * <chr> <int> <chr> <chr> ## 1 Afghanistan 1999 745 19987071 ## 2 Afghanistan 2000 2666 20595360 ## 3 Brazil 1999 37737 172006362 ## 4 Brazil 2000 80488 174504898 ## 5 China 1999 212258 1272915272 ## 6 China 2000 213766 1280428583
A %>%
separate(rate, into = c("cases", "population"), sep = "/")
## # A tibble: 6 x 4 ## country year cases population ## * <chr> <int> <chr> <chr> ## 1 Afghanistan 1999 745 19987071 ## 2 Afghanistan 2000 2666 20595360 ## 3 Brazil 1999 37737 172006362 ## 4 Brazil 2000 80488 174504898 ## 5 China 1999 212258 1272915272 ## 6 China 2000 213766 1280428583
A %>%
separate(rate, into = c("cases", "population"), convert = TRUE)
## # A tibble: 6 x 4 ## country year cases population ## * <chr> <int> <int> <int> ## 1 Afghanistan 1999 745 19987071 ## 2 Afghanistan 2000 2666 20595360 ## 3 Brazil 1999 37737 172006362 ## 4 Brazil 2000 80488 174504898 ## 5 China 1999 212258 1272915272 ## 6 China 2000 213766 1280428583
Unite()
This is a less common operation than separate() (though it is needed for Exercise 5.7). Imagine you were working with this (butchered) form of data set C.
C2 <- C %>%
separate(year, into = c("century", "year_in_century"), sep = 2)
C2
## # A tibble: 6 x 5 ## country century year_in_century cases population ## <chr> <chr> <chr> <int> <int> ## 1 Afghanistan 19 99 745 19987071 ## 2 Afghanistan 20 00 2666 20595360 ## 3 Brazil 19 99 37737 172006362 ## 4 Brazil 20 00 80488 174504898 ## 5 China 19 99 212258 1272915272 ## 6 China 20 00 213766 1280428583
We need to unite() the century and year_in_century columns.
C2 %>% unite(century, year_in_century, col = "year", sep = "")
## # A tibble: 6 x 4 ## country year cases population ## <chr> <chr> <int> <int> ## 1 Afghanistan 1999 745 19987071 ## 2 Afghanistan 2000 2666 20595360 ## 3 Brazil 1999 37737 172006362 ## 4 Brazil 2000 80488 174504898 ## 5 China 1999 212258 1272915272 ## 6 China 2000 213766 1280428583
Tidy data, recap
Primary functions
- If you want to convert data from wide to long, you need to
gather()the columns into values. - If instead you're looking to convert from long to wide, you'll be
spread()ing the values across the columns
Secondary functions
- If you have one column that contains two variables, they need to be
separate()ed. - If you have multiple columns that contain one variable, they need to be
unite()ed.
Writing functions
Functions
Do you ever find yourself with .Rmd files that look like this?
my_df1 %>% ... # do some stuff to my_df1 ... my_df2 %>% ... # do the same stuff to my_df2 ... my_df3 %>% ... # and again to my_df3 ...
For example
What if I want to draw the same kind of plot several times?
my_df1 %>% ggplot(aes(x = var1, y = var2, color = var3)) + geom_point() + geom_line() my_df2 %>% ggplot(aes(x = varA, y = varB, color = varC)) + geom_point() + geom_line() my_df3 %>% ggplot(aes(x = var1A, y = var2B, color = var3C)) + geom_point() + geom_line()
What we really want
User-defined functions
name_of_function <- function(data, var = "value") {
. . .
. . .
<valid R code>
. . .
. . .
return(x)
}
- arguments:
data,vardatais requiredvar is optional - has a default value of"value"`
- returns:
- by default, output of last line in function
- here, explicitly the object
x
Scoping: global variables OK
library(tidyverse)
my_cars <- function(mod) {
mpg %>%
filter(model == mod)
}
my_cars("protege")
## # A tibble: 0 x 11 ## # ... with 11 variables: manufacturer <chr>, model <chr>, displ <dbl>, ## # year <int>, cyl <int>, trans <chr>, drv <chr>, cty <int>, hwy <int>, ## # fl <chr>, class <chr>
Default values
my_cars <- function(mod = "civic") {
mpg %>%
filter(model == mod)
}
my_cars()
## # A tibble: 9 x 11 ## manufacturer model displ year cyl trans drv cty hwy fl class ## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> ## 1 honda civic 1.60 1999 4 manu… f 28 33 r subc… ## 2 honda civic 1.60 1999 4 auto… f 24 32 r subc… ## 3 honda civic 1.60 1999 4 manu… f 25 32 r subc… ## 4 honda civic 1.60 1999 4 manu… f 23 29 p subc… ## 5 honda civic 1.60 1999 4 auto… f 24 32 r subc… ## 6 honda civic 1.80 2008 4 manu… f 26 34 r subc… ## 7 honda civic 1.80 2008 4 auto… f 25 36 r subc… ## 8 honda civic 1.80 2008 4 auto… f 24 36 c subc… ## 9 honda civic 2.00 2008 4 manu… f 21 29 p subc…
Default values overridden
my_cars("jetta")
## # A tibble: 9 x 11 ## manufacturer model displ year cyl trans drv cty hwy fl class ## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> ## 1 volkswagen jetta 1.90 1999 4 manu… f 33 44 d comp… ## 2 volkswagen jetta 2.00 1999 4 manu… f 21 29 r comp… ## 3 volkswagen jetta 2.00 1999 4 auto… f 19 26 r comp… ## 4 volkswagen jetta 2.00 2008 4 auto… f 22 29 p comp… ## 5 volkswagen jetta 2.00 2008 4 manu… f 21 29 p comp… ## 6 volkswagen jetta 2.50 2008 5 auto… f 21 29 r comp… ## 7 volkswagen jetta 2.50 2008 5 manu… f 21 29 r comp… ## 8 volkswagen jetta 2.80 1999 6 auto… f 16 23 r comp… ## 9 volkswagen jetta 2.80 1999 6 manu… f 17 24 r comp…
Naming arguments optional
my_cars("camry") %>%
head(2)
## # A tibble: 2 x 11 ## manufacturer model displ year cyl trans drv cty hwy fl class ## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> ## 1 toyota camry 2.20 1999 4 manu… f 21 29 r mids… ## 2 toyota camry 2.20 1999 4 auto… f 21 27 r mids…
my_cars(mod = "corolla") %>% head(2)
## # A tibble: 2 x 11 ## manufacturer model displ year cyl trans drv cty hwy fl class ## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> ## 1 toyota coro… 1.80 1999 4 auto… f 24 30 r comp… ## 2 toyota coro… 1.80 1999 4 auto… f 24 33 r comp…
Writing functions checklist
Pay attention to:
- names of arguments
- default argument values
- local vs global objects
- return values
Example for real
What does this do?
most_popular_year <- function(data, name_arg) {
data %>%
filter(name == name_arg) %>%
group_by(year) %>%
summarize(total = sum(prop)) %>%
arrange(desc(total)) %>%
head(1) %>%
select(year)
}
library(babynames) most_popular_year(data = babynames, name_arg = "Andrew")
## # A tibble: 1 x 1 ## year ## <dbl> ## 1 1987
most_popular_year(babynames, "Andrew")
## # A tibble: 1 x 1 ## year ## <dbl> ## 1 1987
# most_popular_year("Andrew")
Activity 5
For the following exercises, use the pnwflights14 dataset.
- Write a function that, for a given carrier identifier (e.g. DL), will retrieve the five most common airport destinations from PNW in 2014, and how often the carrier flew there.
- Use your function to find the top five destinations for Alaska Airlines (AS).
- Write a function that, for a given airport code (e.g. LAX), will retrieve the five most common carriers that service that airport from the PNW in 2014, and what their average arrival delay time was.
Data Import
Rectangular Data
Relational Data
Hierarchical Data
